An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields
本文针对美国北部地区平均温度数据，建立了一个平稳时空高斯随机场模型，重点处理协方差结构估计的不确定性，并推导了区域均值随时间变化的后验分布，用于校准历史温度变化。
Abstract In this article we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are temperatures at the stations in the U.S. historical climatological network. The stochastic structure is modeled by a stationary spatial-temporal Gaussian random field. For this region, we find little evidence of temporal dependence while the spatial structure is temporally stable. The approach strives to incorporate the uncertainty in estimating the covariance structure into the predictive distributions and the final inference. As an application of the model, we derive posterior distributions of the areal mean over time. A posterior distribution for the static areal mean is presented as a basis for calibrating temperature shifts by the historical record. For this region and season, the distribution indicates that under the scenario of a gradual increase of 5°F over 50 years, it will take 30–40 winters of data before the change will be discernible from the natural variation in temperatures.